Multiplex assays, involving the simultaneous use of multiple circulating tumor DNA (ctDNA) markers, can improve the performance of liquid biopsies so that they are highly predictive of cancer recurrence. We have developed a single-tube methylation-specific quantitative PCR assay (mqMSP) that uses 10 different methylation markers and is capable of quantitative analysis of plasma samples with as little as 0.05% tumor DNA. In a cohort of 179 plasma samples from colorectal cancer (CRC) patients, adenoma patients, and healthy controls, the sensitivity and specificity of the mqMSP assay were 84.9% and 83.3%, respectively. In a head-to-head comparative study, the mqMSP assay also performed better for detecting early-stage (stage I and II) and premalignant polyps than a published SEPT9 assay. In an independent longitudinal cohort of 182 plasma samples (preoperative, postoperative, and follow-up) from 82 CRC patients, the mqMSP assay detected ctDNA in 73 (89.0%) of the preoperative plasma samples. Postoperative detection of ctDNA (within 2 wk of surgery) identified 11 of the 20 recurrence patients and was associated with poorer recurrence-free survival (hazard ratio, 4.20; P = 0.0005). With subsequent longitudinal monitoring, 14 patients (70%) had detectable ctDNA before recurrence, with a median lead time of 8.0 mo earlier than seen with radiologic imaging. The mqMSP assay is cost-effective and easily implementable for routine clinical monitoring of CRC recurrence, which can lead to better patient management after surgery. Significance Colorectal cancer recurrence is one of the main causes of death. Early prediction of recurrence by minimal residual disease detection and longitudinal tumor monitoring can improve patient outcomes. Next-generation sequencing methods analyzing circulating tumor DNA (ctDNA) in blood can predict recurrence with high accuracy, but these methods are too expensive and complex to be broadly deployable. We have developed a cost-effective and easily implementable single-tube qPCR method (mqMSP) for quantifying a panel of ctDNA methylation markers. Using this panel to monitor patients after surgery can predict cancer recurrence with high accuracy and well in advance of methods in current use.

Small GTPases regulate multiple important cellular behaviors and their activities are strictly controlled by a mass of regulators. The dysfunction or abnormal expression of small GTPases or their regulators was frequently observed in various cancers. Here, we analyzed the expression and prognostic correlation of several GTPases and related regulators based on the TCGA database and found that Ankyrin Repeat and PH Domain 1 (ARAP1), a GTPase activating protein (GAP), is reduced in lung adenocarcinoma tissues compared to normal tissues and displays a positive correlation with overall survival (OS) and progression-free survival (PFS) of patients with lung adenocarcinoma. qPCR and western blot verified that ARAP1 is frequently downregulated in lung adenocarcinoma tumor tissues and cancer cells, and its downregulation might be mediated by epigenetic modification. Moreover, metastatic assays showed that overexpression of ARAP1 significantly inhibits metastasis of lung adenocarcinoma in vitro and in vivo. We further demonstrated that Rho signaling inhibition, mediated by RhoGAP activity of ARAP1, majorly contributes to suppressing migration and invasion of lung adenocarcinoma cancer cells via inhibiting stress fibers formation. In summary, this study indicates that ARAP1 may serve as a potential prognostic predictor and a metastatic suppressor in lung adenocarcinoma via its RhoGAP activity.

Objectives: CA19-9 elevation in non-gastrointestinal tumor patients may be influenced by various non-tumor factors, which poses challenges for clinical diagnosis. This study aims to assess the consistency between initial elevated CA19-9 levels detected by the ARCHITECT/Alinity i system (Abbott Diagnostics) and subsequent retesting using the Elecsys CA19-9 assay (Roche Diagnostics) in 5372 non-gastrointestinal tumor patients, and to explore potential factors contributing to CA19-9 non-specific elevation. Methods: Bland-Altman and Passing-Bablok analyses were used to assess the agreement between the two assays. Nonparametric Spearman and Pearson’s chi-square tests were used to assess the correlation between CA19-9 and different clinical comorbidities/antigen concentration strata and to compare the categorization by age/disease, respectively. Results: Bland–Altman and Passing–Bablok regression analyses revealed that the CA19-9 test results from Abbott and Roche platforms show significant systematic bias and weak correlation, making the two methods not directly interchangeable. After excluding common confounders, the study focused on heterophilic antibodies (HAs) as target. Blood samples were treated with a commercial blocking agent demonstrated alignment with baseline Elecsys CA19-9 results but differed significantly from initial ARCHITECT/Alinity i measurements. Furthermore, non-specific CA19-9 elevation was also associated with comorbidities such as diabetes mellitus, pulmonary infections, breast nodules, uterine leiomyoma, and its incidence increased with age. Conclusions: The study highlights the need to consider potential interferences and underlying disorders when results conflict with clinical diagnoses. Method-specific validation and comprehensive clinical correlation are crucial for accurate interpretation of CA19-9 levels to prevent misdiagnosis and ensure appropriate patient management.

Background: Upper tract urothelial carcinoma (UTUC) is a rare but highly invasive urinary malignancy with a high postoperative recurrence rate.Methods: We retrospectively collected data from 133 UTUC patients who underwent radical nephroureterectomy between 2005 and 2017. Patients were divided into a training set (n=103) and a testing set (n=30). A multi-modal deep learning model named Multi-modal Image-Clinical Combination Classifier (MICC) was developed by integrating multi-phase contrast-enhanced CT imaging and clinical data. The model’s prognostic performance was compared with two unimodal models—ImageNet (CT-based) and ClinicalNet (clinical data-based)—and traditional clinical parameters including pathological T stage. Feature importance was evaluated using SHapley Additive exPlanations (SHAP).Results: The MICC model achieved superior prognostic accuracy with AUCs of 0.918 and 0.895 in the training and testing sets, respectively, outperforming unimodal models. Classification metrics were robust, with accuracy of 0.854, sensitivity of 0.889, specificity of 0.836, negative predictive value (NPV) of 0.933, and positive predictive value (PPV) of 0.744. Precision-recall analysis confirmed strong identification of high-risk patients despite dataset imbalance. SHAP analysis highlighted that CT imaging features contributed most significantly to the model’s predictions.Conclusion: Integrating multi-phase CT imaging with clinical data, the MICC model provides accurate prognostic prediction for UTUC patients. This approach has potential to assist clinicians in personalized risk stratification and treatment planning, ultimately improving patient outcomes.